Electronic systems and circuits have made a significant contribution towards the advancement of modern society and are utilized in a number of applications to achieve advantageous results. Numerous electronic technologies such as digital computers, calculators, audio devices, video equipment, and telephone systems have facilitated increased productivity and reduced costs in analyzing and communicating data, ideas and trends in most areas of business, science, education and entertainment. Frequently, electronic systems designed to provide these advantageous results are realized through the leveraged utilization of centralized resources by distributed network nodes. While leveraged utilization of centralized resources is usually advantageous, management of centralized resource operations is usually very complex and often involves significant infrastructure support coordination and maintenance.
Centralizing certain resources within a distributed network typically provides desirable benefits. For example, centrally storing and/or processing information typically relieves the necessity of wasteful duplicative storage and/or processing resources at each remote networked node. However, managing large storage and processing capabilities of centralized resources is very complex and expensive. Clients interested in engaging a host to provide centralized resources and services typically have a desire to avoid providing the infrastructure, operation and maintenance directly themselves.
Centralized computing resource centers (e.g., server farms, Application Service Provider Centers, Internet Data Centers, Utility Data Centers, etc.) usually include a variety of equipment related to information processing mounted in racks. For example, a rack can include servers, routers, disk arrays, and operational support components (e.g., power distribution components, fans, etc.). The racks usually provide a convenient and efficient way to arrange computing equipment in a centralized operation location. The configuration of the rack structures usually follow conventional standards. However, the equipment mounted within a rack can vary dramatically. Managing and maintaining the infrastructure to support numerous different pieces of equipment and possible applications in a large and complicated centralized resource environment for a variety of different clients raises many challenging operational issues. In addition, there are a variety of infrastructure support activities that can have a significant impact on rack equipment operation in a centralized resource location.
Traditional attempts at addressing the management and maintenance issues in conventional centralized resource centers are usually labor intensive and difficult. For example, some traditional attempts involve manually correlating numerous different types of information, including absolute maximum ratings defined by regulatory requirements of power supply vendors, amount of current flowing through a branch circuit, manual partial configuration of rack equipment, impacts of partial configuration on performance, local environmental conditions, cost of maintaining power and thermal envelopes, and economic value of computing services running on the rack equipment. Collecting, correlating, and analyzing this information manually is labor intensive and requires a significant level of specialized knowledge and expertise.
Attempting to manually address problems that can arise is often complicated and complex. The sheer number of different possible characteristics of the different pieces of rack equipment and rack support equipment presents daunting manual tracking and organizing issues. This is further complicated by the variety of possible performance settings that each piece of rack equipment and support equipment may be capable of. For example, conventional attempts usually have difficulty adjusting performance with respect to power consumption and thermal loading characteristics since the performance levels of centralized processing related equipment are typically fixed.
Even if a traditional rack equipment adjustment decision is made, implementing the adjustments manually is also typically resource intensive. Manually adjusting the rack equipment usually requires the operator to have knowledge and understanding of unique features of each piece of equipment. The traditional response time is also often slow when compared to the speed at which processing operations occur and/or change in the rack equipment. Traditional manual attempts at speeding up the adjustments in an environment with a variety of numerous different pieces of rack equipment tends to increase the probability of human error in making the adjustments.